Triple

T974621
Position Surface form Disambiguated ID Type / Status
Subject Michael E21023 entity
Predicate hasVariant P455 FINISHED
Object Mikel
Mikel is a given name, commonly a variant of Michael used in various cultures, particularly in Basque and Spanish-speaking regions.
 E114789 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mikel | Statement: [Michael, hasVariant, Mikel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mikel
Context triple: [Michael, hasVariant, Mikel]
  • A. Álvaro
    Álvaro is a masculine given name of Spanish origin commonly used in Spain and Latin America.
  • B. Carvajal
    Carvajal is a Spanish surname of likely toponymic origin, borne by various notable figures in Spanish and Latin American history.
  • C. Jorge
    Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
  • D. Roberto
    Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
  • E. Marcelo
    Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mikel
Triple: [Michael, hasVariant, Mikel]
Generated description
Mikel is a given name, commonly a variant of Michael used in various cultures, particularly in Basque and Spanish-speaking regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mikel
Target entity description: Mikel is a given name, commonly a variant of Michael used in various cultures, particularly in Basque and Spanish-speaking regions.
  • A. Álvaro
    Álvaro is a masculine given name of Spanish origin commonly used in Spain and Latin America.
  • B. Carvajal
    Carvajal is a Spanish surname of likely toponymic origin, borne by various notable figures in Spanish and Latin American history.
  • C. Jorge
    Jorge is the birth name of Pope Francis, the head of the Roman Catholic Church and the first pope from the Americas.
  • D. Roberto
    Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
  • E. Marcelo
    Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a493c2b62c8190b616351789ec47f8 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b460a5c0819087b03dfb8a3af2c2 completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac170c0fdc8190b904ca5737764f5a completed March 7, 2026, 12:16 p.m.
NEDg Description generation batch_69ac17c2e6f48190be6fce7f279957c4 completed March 7, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_69ac1844acec81909859605d2421a588 completed March 7, 2026, 12:21 p.m.
Created at: March 1, 2026, 7:40 p.m.